36 citations · 37 across the 5 of their papers we have counts for
5 papers
NeurIPS 2024 ML4CFD Competition: Results and Retrospective Analysis
Mouadh Yagoubi, David Danan, Milad Leyli-Abadi +15
The integration of machine learning (ML) into the physical sciences is reshaping computational paradigms, offering the potential to accelerate demanding simulations such as computa…
Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations
Giovanni Catalani, Jean Fesquet, Xavier Bertrand +3
This paper introduces a novel surrogate modeling framework for aerodynamic applications based on Neural Fields. The proposed approach, MARIO (Modulated Aerodynamic Resolution Invar…
PLAID: A Unified Data Model for Machine Learning on Heterogeneous Physics Simulations
Fabien Casenave, Xavier Roynard, Brian Staber +17
Machine learning-based surrogate models have emerged as a powerful tool to accelerate simulation-driven scientific workflows, but their adoption is limited by the lack of large-sca…
Geometry aware inference of steady state PDEs using Equivariant Neural Fields representations
Giovanni Catalani, Michael Bauerheim, Frédéric Tost +2
Advances in neural operators have introduced discretization invariant surrogate models for PDEs on general geometries, yet many approaches struggle to encode local geometric struct…
Aero-Nef: Neural Fields for Rapid Aircraft Aerodynamics Simulations
Giovanni Catalani, Siddhant Agarwal, Xavier Bertrand +3
This paper presents a methodology to learn surrogate models of steady state fluid dynamics simulations on meshed domains, based on Implicit Neural Representations (INRs). The propo…